Excretion of tiapamil in breast milk

Molecular descriptors that influence the amount of drugs transfer into human breast milk

Most drugs are excreted into breast milk to some extent and are bioavailable to the infant. The ability to predict the approximate amount of drug that might be present in milk from the drug structure would be very useful in the clinical setting. The aim of this research was to simplify and upgrade the previously developed model for prediction of the milk to plasma (M/P) concentration ratio, given only the molecular structure of the drug. The set of 123 drug compounds, with experimentally derived M/P values taken from the literature, was used to develop, test and validate a predictive model. Each compound was encoded with 71 calculated molecular structure descriptors, including constitutional descriptors, topological descriptors, molecular connectivity, geometrical descriptors, quantum chemical descriptors, physicochemical descriptors and liquid properties. Genetic algorithm was used to select a subset of the descriptors that best describe the drug transfer into breast milk and artificial neural network (ANN) to correlate selected descriptors with the M/P ratio and develop a QSAR. The averaged literature M/P values were used as the ANN's output and calculated molecular descriptors as the inputs. A nine-descriptor nonlinear computational neural network model has been developed for the estimation of M/P ratio values for a data set of 123 drugs. The model included the percent of oxygen, parachor, density, highest occupied molecular orbital energy (HOMO), topological indices (chiV2, chi2 and chi1) and shape indices (kappa3, kappa2), as the inputs had four hidden neurons and one output neuron. The QSPR that was developed indicates that molecular size (parachor, density) shape (topological shape indices, molecular connectivity indices) and electronic properties (HOMO) are the most important for drug transfer into breast milk. Unlike previously reported models, the QSPR model described here does not require experimentally derived parameters and could potentially provide a useful prediction of M/P ratio of new drugs only from a sketch of their structure and this approach might also be useful for drug information service. Regardless of the model or method used to estimate drug transfer into breast milk, these predictions should only be used to assist in the evaluation of risk, in conjunction with assessment of the infant's response.

Calcium channel antagonists and lactation: an update

Approximately 14% of women of age 20-54 years old have hypertension. For those women who are on medication for hypertension and are pregnant, this poses potential problems. In many cases, antihypertensive agents may be continued in women with chronic hypertension if taken prior to pregnancy. This article discusses the antihypertensive class, calcium channel antagonists, with regard to their physiochemical properties and lactation.

Prospective evaluation of a model for the prediction of milk:plasma drug concentrations from physicochemical characteristics

1. Milk:plasma (M/P) drug concentration ratios predicted by a model utilizing pKa, plasma protein binding and octanol:water partition coefficients have been compared with actual M/P values for 10 basic drugs. 2. There was a close relationship between predicted and observed M/P ratios with a coefficient of determination r2 of 0.97. However, there was a proportional error. 3. The data were transformed by taking logs of predicted and observed (M/P + 1) values. Regression analysis resulted in an r2 of 0.95, an intercept on the Y-axis not significantly different from zero and a slope not significantly different from one. 4. The 95% confidence interval around a single prediction revealed an error between 150% for the lowest and 23% for the highest M/P ratios. The error is therefore lowest for the drugs likely to have the greatest transfer into milk. 5. There was no significant bias in the predictions. 6. The model was refined by multiple linear regression analysis utilising the observed M/P ratios for the 10 basic drugs in addition to those of the original drugs. The revised equation resulted in an improvement in the explained variance. 7. Protein binding was the most important single predictor. 8. The results confirm that M/P ratios for basic drugs can be predicted accurately from their physicochemical characteristics.